21 Lessons, Get Started Building with Generative AI
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12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
- ml
- data-science
- machine-learning
- machine-learning-algorithms
12 Weeks, 24 Lessons, AI for All!
10 Weeks, 20 Lessons, Data Science for All!
aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)
- bayesian-methods
- pymc
- mathematical-analysis
- jupyter-notebook
Python programs, usually short, of considerable difficulty, to perfect particular skills.
- python
- python-3
- programming
- practice
Learn OpenCV : C++ and Python Examples
Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.
12 Weeks, 24 Lessons, IoT for All!
Dive into this repository, a comprehensive resource covering Data Structures, Algorithms, 450 DSA by Love Babbar, Striver DSA sheet, Apna College DSA Sheet, and FAANG Questions! 🚀 That's not all! We've got Technical Subjects like Operating Systems, DBMS, SQL, Computer Networks, and Object-Oriented Programming, all waiting for you.
- interview-questions
- dsa
- dbms
- sql
All course materials for the Zero to Mastery Machine Learning and Data Science course.
All course materials for the Zero to Mastery Deep Learning with TensorFlow course.
- deep-learning
- deep-neural-networks
- tensorflow
- tensorflow2
Think DSP: Digital Signal Processing in Python, by Allen B. Downey.
- dsp
- python
- signal-processing
- textbook
TensorFlow Basic Tutorial Labs
- tensorflow
- keras
- mxnet
- pytorch
Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Networks; (v) Sequence Models
- deep-learning
- coursera
- coursera-assignment
- coursera-specialization
Text and supporting code for Think Stats, 2nd Edition
- data-science
- python
- statistics
- textbook
This is a Phi Family of SLMs book for getting started with Phi Models. Phi a family of open sourced AI models developed by Microsoft. Phi models are the most capable and cost-effective small language models (SLMs) available, outperforming models of the same size and next size up across a variety of language, reasoning, coding, and math benchmarks
- phi3
- phi3-testing
- phi3-vision
- phi4
All notes and materials for the CS229: Machine Learning course by Stanford University
- machine-learning
- stanford-university
- neural-networks
- cs229
Open-source demos hosted on Dash Gallery
- plotly-dash
- dash
- plotly
Repository of Jupyter notebook tutorials for teaching the Deep Learning Course at the University of Amsterdam (MSc AI), Fall 2023
- deep-learning
- tutorials
- uvadlc
- pytorch
The repository is about 100+ python programming exercise problem discussed, explained, and solved in different ways
- python
- programming
- learning-exercise
- python-programming-exercises
Quantitative analysis, strategies and backtests
- quantitative-finance
- quantitative-trading
- portfolio-management
- risk-management
📊⚽ A collection of football analytics projects, data, and analysis by Edd Webster (@eddwebster), including a curated list of publicly available resources published by the football analytics community.
- football
- xg
- expected-goals
- statsbomb
Examples, end-2-end tutorials and apps built using Liquid AI Foundational Models (LFM) and the LEAP SDK